Film Cooling Prediction and Optimization Based on Deconvolution Neural Network

For film cooling in high pressure turbines, it is vital to predict the temperature distribution on the blade surface downstream of the cooling hole. This temperature distribution depends on the interaction between the hot mainstream and the coolant jet. Deep learning techniques have been widely applied in predicting physical problems such as complex fluids dynamics. A theoretic model based on Deconvolutional Neural Network (Deconv NN) was developed to model the non-linear and high-dimensional mapping between coolant jet parameters and the surface temperature distribution. Computational Fluid Dynamics (CFD) was utilized to provide data for the training models. The input of the model includes blowing ratio, density ratio, hole inclination angle and hole diameters etc. Comparison against different methods and data set size for accuracy is conducted and the result shows that the Deconv NN is capable of predicting film cooling effectiveness on the surface in validation group with quoted error (QE) less than 0.62%. With rigorous testing and validation, it is found that the predicted results are in good agreement with results from CFD. At the end, the Sparrow Search Algorithm (SSA) is applied to optimize coolant jet parameters using the validated neural networks. The results of the optimization show that the film cooling effectiveness has been successfully improved with QE 7.35% when compared with the reference case.For film cooling in high pressure turbines, it is vital to predict the temperature distribution on the blade surface downstream of the cooling hole. This temperature distribution depends on the interaction between the hot mainstream and the coolant jet. Deep learning techniques have been widely applied in predicting physical problems such as complex fluids dynamics. A theoretic model based on Deconvolutional Neural Network (Deconv NN) was developed to model the non-linear and high-dimensional mapping between coolant jet parameters and the surface temperature distribution. Computational Fluid Dynamics (CFD) was utilized to provide data for the training models. The input of the model includes blowing ratio, density ratio, hole inclination angle and hole diameters etc. Comparison against different methods and data set size for accuracy is conducted and the result shows that the Deconv NN is capable of predicting film cooling effectiveness on the surface in validation group with quoted error (QE) less than 0.62%. With rigorous testing and validation, it is found that the predicted results are in good agreement with results from CFD. At the end, the Sparrow Search Algorithm (SSA) is applied to optimize coolant jet parameters using the validated neural networks. The results of the optimization show that the film cooling effectiveness has been successfully improved with QE 7.35% when compared with the reference case.